Methods for processing and prioritizing customer demands in variant product design

Abstract

In the current highly competitive marketplace, customer demand is a major factor in the product design process. Many methods, such as quality function deployment and House of Quality (HoQ), provide a powerful process for translating and mapping customer demands into technical requirements. In the HoQ process, the customers' perceptions and expectations should be evaluated together in order to identify desirable product features. Moreover, in variant product design, the priority of each feature needs to be determined based on the customers' ratings of both the feature importance and customers' satisfaction. Although many methods such as quality attribute ranking, potential gain in customer value index and the analytic hierarchy process have been previously used to determine the relative importance of customer demands, they do not offer specific methods to determine a revised priority for product redesign. To address this issue, this research focuses on not only determining, but also revising the priority of customer demands for a variant product design based on new customer surveys. Two methods are proposed in this research. The first method classifies customer demands using natural language processing techniques in order to obtain customer expectations. Once comprehensive customer demands are obtained, the second method determines the revised priority of the customer demands using a fuzzy logic inference. These methods are implemented by two computerized systems, a customer demand decomposition and a classification system, and a customer demand priority rating system, with user-friendly interfaces. An example for car redesign is used to demonstrate the proposed methods. With the use of these two systems, the collection and revision of prioritization of the customer demands can be accomplished effectively.